Vegetation traffic barriers along roads can be an effective structure to improve roadside air quality and to reduce human exposure to traffic air pollutants. However, the selection of the plant species should be considered as an important design parameter for vegetation traffic barriers because different plant species demonstrate different levels of tolerance to air pollutants. This study compares the air pollution tolerance of different plant species found in the vegetation traffic barriers in the Kathmandu valley. Four biochemical parameters (relative water content, leaf extract pH, total chlorophyll and ascorbic acid) and the dust-capturing potential of plants were analyzed. Out of the nine selected species, Cinnamomum camphora showed the highest tolerance to air pollution based on the air pollution tolerance index. Similarly, Schefflera pueckleri, Psidium guajava and Ficus benjamina were found to be the sensitive species, while Ficus sp., Nerium oleander, Thuja sp., Dypsis lutescens and Albizia julibrissin were found to have a moderate level of tolerance to air pollution. N. oleander had the highest dust-capturing potential. Considering both air pollution tolerance index and dust-capturing potential, C. camphora, N. oleander and A. julibrissin were found to be the most suitable species for the roadside plantation. The findings of this study might have important implications for plant species selection for vegetation traffic barriers.

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The deterioration of surface water quality occurs due to the presence of various types of pollutants from human activities such as agriculture, industry, construction, deforestation, etc. Thus, the presence of various pollutants in water bodies can lead to deterioration of both surface water quality and aquatic life. Conventional surface water quality assessment methods are widely performed using laboratory analysis, which are labour intensive, costly, and time consuming. Moreover, these methods can only provide individual concentration of surface water quality parameters (SWQPs), measured at monitoring stations and shown in a discrete point format, which are difficult for decision-makers to understand without providing the overall patterns of surface water quality. To such problem, Remote Sensing has been a blessing because of its low cost, spatial continuity and temporal consistency. The relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. This study aims to develop techniques for estimating the concentration of both optical and non-optical SWQPs from Satellite Imagery (Landsat8) which supports coastal studies and mapping the complex relationship between satellite multi-spectral signature and concentration of SWQPs. It will also focus on classifying the most significant SWQPs that contribute to both spatial and temporal surface water quality. In contrast to traditionally performed surface water quality assessment methods, this research project will be focused on identifying such parameters incorporating the new and evolving machine intelligence that is Artificial Intelligence (AI). Significant number of samples have to be collected along with the GPS data which is used to model the relationship. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) will be developed to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. The study area chosen for this research is Bijayapur River of distance approximately 10 km flowing above, through and down the Pokhara city. The sole purpose of this research is to examine the water quality before it flows through the city and analysing after it passes through the city.

Career psychology is placing an increasingly greater focus on culture-resonant theories of development and culture-concordant career interventions. This chapter describes the cultural preparation process model (CPPM) as a framework to understand how culture mediates the process by which individuals and communities engage with their careers and livelihood. The key propositions of the CPPM are presented along with its applicational dimension. The model as a template for intervention development is discussed, and five guidelines—recognizing cultural leadership, expanding the definition of “client,” identifying and accommodating ways of living, valorizing cultural symbols, and integrating livelihood and career—are described. Jiva, an intervention based on the CPPM, and its impact and outcomes are presented, with evidence of outcomes from India and adaptations implemented in other countries. Applying the CPPM to assess interests and aptitudes is considered, with the Strengths and Accomplishments Questionnaire presented as an example. In summary, this chapter provides a reference point from which culture could be drawn into the career development discourse.